#!/usr/bin/env python2
# -*- coding: utf-8 -*-


import torch
import torch.nn as nn
import torch.nn.functional as F
from network import Conv2d, FC


class CMTL(nn.Module):
    '''
    Implementation of CNN-based Cascaded Multi-task Learning of High-level Prior and Density
    Estimation for Crowd Counting (Sindagi et al.)
    '''
    def __init__(self, bn=False, num_classes=10):
        super(CMTL, self).__init__()
        
        self.num_classes = num_classes        
        self.base_layer = nn.Sequential(Conv2d( 1, 16, 9, same_padding=True, NL='prelu', bn=bn),                                     
                                        Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn))
        
        self.hl_prior_1 = nn.Sequential(Conv2d( 32, 16, 9, same_padding=True, NL='prelu', bn=bn),
                                     nn.MaxPool2d(2),
                                     Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn),
                                     nn.MaxPool2d(2),
                                     Conv2d(32, 16, 7, same_padding=True, NL='prelu', bn=bn),
                                     Conv2d(16, 8,  7, same_padding=True, NL='prelu', bn=bn))
                
        self.hl_prior_2 = nn.Sequential(nn.AdaptiveMaxPool2d((32,32)),
                                        Conv2d( 8, 4, 1, same_padding=True, NL='prelu', bn=bn))
        
        self.hl_prior_fc1 = FC(4*1024,512, NL='prelu')
        self.hl_prior_fc2 = FC(512,256,    NL='prelu')
        self.hl_prior_fc3 = FC(256, self.num_classes,     NL='prelu')
        
        
        self.de_stage_1 = nn.Sequential(Conv2d( 32, 20, 7, same_padding=True, NL='prelu', bn=bn),
                                     nn.MaxPool2d(2),
                                     Conv2d(20, 40, 5, same_padding=True, NL='prelu', bn=bn),
                                     nn.MaxPool2d(2),
                                     Conv2d(40, 20, 5, same_padding=True, NL='prelu', bn=bn),
                                     Conv2d(20, 10, 5, same_padding=True, NL='prelu', bn=bn))
        
        self.de_stage_2 = nn.Sequential(Conv2d( 18, 24, 3, same_padding=True, NL='prelu', bn=bn),
                                        Conv2d( 24, 32, 3, same_padding=True, NL='prelu', bn=bn),                                        
                                        nn.ConvTranspose2d(32,16,4,stride=2,padding=1,output_padding=0,bias=True),
                                        nn.PReLU(),
                                        nn.ConvTranspose2d(16,8,4,stride=2,padding=1,output_padding=0,bias=True),
                                        nn.PReLU(),
                                        Conv2d(8, 1, 1, same_padding=True, NL='relu', bn=bn))
        
    def forward(self, im_data):
        x_base = self.base_layer(im_data)
        x_hlp1 = self.hl_prior_1(x_base)
        x_hlp2 = self.hl_prior_2(x_hlp1)
        x_hlp2 = x_hlp2.view(x_hlp2.size()[0], -1) 
        x_hlp = self.hl_prior_fc1(x_hlp2)
        x_hlp = F.dropout(x_hlp, training=self.training)
        x_hlp = self.hl_prior_fc2(x_hlp)
        x_hlp = F.dropout(x_hlp, training=self.training)
        x_cls = self.hl_prior_fc3(x_hlp)        
        x_den = self.de_stage_1(x_base)        
        x_den = torch.cat((x_hlp1,x_den),1)
        x_den = self.de_stage_2(x_den)
        return x_den, x_cls